Explicitly calling out that they are not going to train on enterprise's data and SOC2 compliance is going to put a lot of the enterprises at ease and embrace ChatGPT in their business processes.
From our discussions with enterprises (trying to sell our LLM apps platform), we quickly learned how sensitive enterprises are when it comes to sharing their data. In many of these organizations, employees are already pasting a lot of sensitive data into ChatGPT unless access to ChatGPT itself is restricted. We know a few companies that ended up deploying chatbot-ui with Azure's OpenAI offering since Azure claims to not use user's data (https://learn.microsoft.com/en-us/legal/cognitive-services/o...).
We ended up adding support for Azure's OpenAI offering to our platform as well as open-source our engine to support on-prem deployments (LLMStack - https://github.com/trypromptly/LLMStack) to deal with the privacy concerns these enterprises have.
My company (Fortune 500 with 80,000 full time employees) has a policy that forbids the use of any AI or LLM tool. The big concern listed in the policy is that we may inadvertently use someone else’s IP from training data. So, our data going into the tool is one concern, but the other is our using something we are not authorized to use because the tool has it already in its data. How do you prove that that could never occur? The only way I can think of is to provide a comprehensive list of everything the tool was trained on.
It’s a legal unknown. There’s nothing more to it. Your employer has opted for one side of the coin flip, and it’s the risk averse-one. Any reasonably-sized org is going to be raising the same questions, but instead opting to reap the benefits and take on the legal risk, which is something organisations do all the time anyway.
For me that discussion is always hard to grasp. When a human would learn coding autodidacticly by reading source code, and later they would write new code — then they could only do so because they read licensed code. No one would ask for the license, right?
Because humans aren't computers and the similarities between the two, other than the overuse of the word "learning" in the computer's case, are nonexistant?
Don't know if they are, and don't really care either and I don't care to anthropomorphize circuitry to the extent that AI proponents tend to, especially.
Humans and Computers are 2 wholly separate entities, and there's 0 reason for us to conflate the two. I don't care if another human looks at my code and straight up copies/pastes it, I care very much if an entity backed by a megacorp like Micro$oft does the same, en-masse, and sells it for profit, however.
Okay, so the scale at which they sale their service is a good argument that this is different from a human learning.
However, on the other hand we also have the scale at which they learn, which kind of makes every individual source line of code they learn from pretty unimportant. Learning at this scale is statistical process, and in most cases individual source snippets diminish in the aggregation of millions of others.
Or to put it the other way round, the actual value lies in the effort of collecting the samples, training the models, creating the software required for the whole process, putting everything into a good product and selling it. Again, in my mind, the importance of every individual source repo is too small at this scale to care about their license.
The idea that individual source snippets at this scale diminish in aggregation, is undercut by the fact that OpenAI and MSFT are both selling enterprise-flavoured versions of GPT, and the one thing they promise is that enterprise data will not be used to further train GPT.
That is a fear for companies because the individual source snippets and the knowledge "learned" from them is seen as a competitive advantage of which the sources are an integral part - and I think this is a fair point from their side. However then the exact same argument should apply in favour of paying the artists, writers, coders etc whose work has been used to train these models.
So it sounds like they are trying to have their cake and eat it too.
Hmm. You sure this is the same thing? I would say it’s more about confidentiality than about value.
Because what companies want to hide are usually secrets, that are available to (nearly) no one outside of the company. It’s about preventing accidental disclosure.
What AIs are trained on, on the other hand, is publicly available data.
To be clear: what could leak accidentally would have value of course. But here it’s about the single important fact that gets public although it shouldn’t, vs. the billions of pieces from which the trained AI emerges.
It's really not different in scale. Imagine for a moment how much storage space it would take to store the sensory data that any two year old has experienced. That would absolutely dwarf the text-based world the largest of LLMs have experienced.
But that also exists in the AI world. It’s called „fine tuning“: a LLM trained on a big general dataset can learn special knowledge with little effort.
I’d guess it’s exactly the same with humans: a human that received good general education can quickly learn specific things like C.
Humans have experienced an amount of data that absolutely dwarfs the amount of data even the largest of LLMs have seen. And they've got billions of years of evolution to build on to boot
The process of evolution "from scratch", i.e. from single-celled organisms took billions of years.
This is all relevant because humans aren't born as random chemical soup. We come with pre-trained weights from billions of years of evolution, and fine-tune that with enormous amounts of sensory data for years. Only after that incredibly complex and time-consuming process does a person have the ability to learn from a few examples.
An LLM can generalize from a few examples on a new language that you invent yourself and isn't in the training set. Go ahead and try it.
There is the element of the unknown with LLMs etc.
There is a legal difference between learning from something and truly making your own version and simply copying.
It's vague of course - take plagiarism in a university science essay - the student has no original data and very likely no original thought - but still there is a difference between simply copying a textbook and writing it in your own words.
Bottom line - how do we know the output of the LLM isn't a verbatim copy of something with the license stripped off?
The way I see it is that with AI you have really painted your own Caravaggio, but instead of an electrochemical circuit of a human brain you've employed a virtual network.
> but instead of an electrochemical circuit of a human brain you've employed a virtual network.
technically it is still a tool you are using, differently from doing it on your own, with your hands, using your own brain cells, that you trained over the decades, instead of using a virtual electronic brain pre-trained in hours/days by someone else on who knows what.
Okay if it’s about looking at one painting and fake that. However, if you train your model on billions of paintings and create arbitrary new ones from that, it’s just a statistical analysis on what paintings in general are made of.
The importance of the individual painting diminishes at this scale.
I'd add to this, the damage an LLM could do is much less than a human could do in terms of individual production. A person can paint so many forgeries... A machine can create many, many more. The dilusion of value from a person learning is far different than machine learning. The value extracted and diluted is night and day in terms of scale.
Not to say what will/won't happen. In practice, what I've seen doesn't scare me much in terms of what LLMs produce vs. what a person has to clean up after it's produced.
They are not the same because an LLM is a construct. It is not a living entity with agency, motive, and all the things the law was intended for.
We will see new law as this tech develops.
For an analogy, many people call infringement theft and they are wrong to do so.
They will focus on the someone getting something without having followed the right process part while ignoring the equally important someone else being denied the use of, or loss of property part.
The former is an element in common between theft and infringement. And it is compelling!
But, the real meat in theft is all about people losing property! And that is not common at all.
This AI thing is similar. The common elements are super compelling.
But it just won't be about that in the end. It will be all about the details unique to AI code.
Using the word "construct" isn't adding anything to the conversation. If we bioengineer a sentient human, would you feel OK torturing it because it's "just a construct"? If that's unethical to you, how about half meat and half silicon? How much silicon is too much silicon and makes torture OK?
> Most people will [privilege meat]
"A person is smart. People are dumb, panicky dangerous animals, and you know it". I agree that humans are likely to pass bad laws, because we are mostly just dumb, panicky dangerous animals in the end. That's different than asking an internet commentor why they're being so confident in their opinions though.
Full stop. We've not done that yet. When we do, we can revisit the law / discussion.
We can remedy "construct" this way:
Your engineered human would be a being. Being a being is one primary difference between us and these LLM things we are toying with right now.
And yes, beings are absolutely going to value themselves over non beings. It makes perfect sense to do so.
These LLM entities are not beings. That's fundamental. And it's why an extremely large number of other beings are going to find your comment laughable. I did!
You are attempting to simplify things too much to be meaningful.
Define "being". If it's so fundamental, it should be pretty easy, no?
And I'd like if this were simple. Unfortunately there's too many people throwing around over-simplifications like "They are not the same because an LLM is a construct" or "These LLM entities are not beings". If you'll excuse the comparison, it's like arguing with theists that can't reason about their ideological foundations, but can provide specious soundbites in spades.
A being is a living thing with a will to survive, need for food, and a corporeal existence, in other words, is born, lives for a time, then dies.
Secondly, beings are unique. Each one has a state that ends when they do and begins when they do. So far, we are unable to copy this state. Maybe we will one day, but that day, should there ever be one, is far away. We will live our lives never seeing this come to pass.
Finally, beings have agency. They do not require prompting.
Also twice now you've said the equivalent of "it hasn't happened yet so no need to think about the implications". Respectfully, I think you need to ponder your arguments a bit more carefully. Cheers.
They've got a few fantastic attributes, lots of different beings do. You know the little water bear things are tough as nails! You can freeze them for for a century wake them up and they'll crawl around like nothing happened.
Naked mole rats don't get any form of cancer. All kinds of things the beans present in the world that doesn't affect the definition at all.
You didn't gain any ground with that.
And I will point out, it is you who has the burden in this whole conversation. I am clearly in the majority if you want things with what I've said. And I will absolutely privilege meets face over silicon any day, for the reasons I've given.
You, on the other hand, have a hell of a sales job ahead of you. Good luck maybe this little exchange helped a bit take care
> Or do they magically become "beings" when they die?
quoting from your link
although in practice individuals can still die. In nature, most Turritopsis dohrnii are likely to succumb to predation or disease in the medusa stage without reverting to the polyp form
This sentence does not apply to an LLM.
Also, you can copy an LLM state and training data and you will have an equivalent LLM, you can't copy the state of a living being.
Mostly because a big chunk of the state is experience, like for example you take that jellyfish, cut one of its tentacles and it will be scarred for life (immortal or not). That can't be copied and most likely never will.
Regarding the copying of a being state, I'm not really sure that's ever even going to be possible.
So for the sake of argument I'll just amend that and say we can't copy their state. Each being is unique and that's it. They aren't something we copy.
And yes that means all of us that thinks somehow they're going to get downloaded into a computer? I'll say it right here and now that's not going to fucking happen.
> The only way I can think of is to provide a comprehensive list of everything the tool was trained on.
There are some startups working in the space that essentially plan to do something like this. https://www.konfer.ai/aritificial-intelligence-trust-managem... is one I know of that is trying to solve this. They enable these foundation model providers to maintain an inventory of training sources so they can easily deal with coming regulations etc.
Microsoft/OpenAI are selling a service. They’re both reputable companies. If it turns out that they are reselling stolen data, are you really liable for purchasing it?
If you buy something that fell of a truck, then you are liable for purchasing stolen goods. But if it turns out that all the bananas in wall mart were stolen from cosco you’re not as a customer liable for theft.
Similarly, I don’t know if Clarkson Intelligence have purchased proper license for all the data they are reselling. Maybe they are also scraping some proprietary source and now you are using someone else’s IP.
Even if you find a way to successfully forward liability and damages to Microsoft and OpenAI - which I doubt you will be able to as the damages are determined by your use of the IP - you do not gain the right to use the affected IP and will have a cease and desist for whatever is built upon it.
How legitimate the IP concern is and whether it holds up in court is one thing, but finger pointing will probably not be sufficient.
Realistically you can prove that just as well as you can prove that employees aren't using ChatGPT via their cellphones.
There are also organizations that forbid the use of Stack overflow. As long as employees don't feel like you're holding back their career and skills by prohibiting them from using modern tools, and keep working there, hey. As long as you pay them enough to stay, people will put up with a lot, even if it hurts them.
Using $technological_aide \in {chatgpt,ide,stackoverflow,google,debuggers,compilers,optimizers,high-mem VMs}$ to code is not a skill. It’s a crutch. Any employees that feel held back by not being able to access it aren’t great in the first place.
I don't think using ChatGPT is similar to searching for answers on S.O. Maybe if you were asking people on S.O. to write your code for you, or plugging in exact snippets. The point here is that letting ChatGPT write code directly into your repo is effectively plagiarism and may violate any number of licenses you don't even realize you're breaking, whereas just looking at how other people did something, understanding, and then writing your own code, does not.
Honestly I couldn’t tell you whether copying code out of Stack Overflow or out of ChatGPT is more legally suspect. For SO, you don’t know where the user got the code from either (wrote it themselves? Copied it out of their work repo? Copied from some random GPL source?)
I've been experiencing carpal tunnel on and off for a couple of weeks now.
I can tell you that reading through some code generated by "insert llm x" is substantially less painful than writing all of it by my own hand.
Especially if you start understanding how to refine your prompts to the point where you use a single thread for project management and use that thread to generate prompts for other threads.
Not all value to be gained from this is purely copypasta.
Same here. End of last year I had to take more time off than I wanted to because of my wrists and hands. Copilot and GPT4 (combined with good compression braces) got me back in the game.
Stackoverflow yes, the others aren’t the same category. Asking someone else to give you code to do X means you have struggled synthesizing algorithms yourself. It’s not a good habit because it means you struggle to be precise in how the program behaves.
I am not a programmer and only know some very rudimentary HTML and Java. After hearing everyone enthuse about how they use ChatGPT for everything, I thought that I could use it to generate a page that I thought sounded simple enough. Gist of it was that I needed 100 boxes of the same dimensions that text could be inputted into. I figured that it'd be faster with AI than with me setting up an excel sheet that others would have to access.
Instead, the AI kept spitting out slightly-off code, and no matter how much reiterations I did it did not improve. Had I known the programming language, I would have known what needed to be changed. I think that a lot of highly experienced people are using it as a short-hand to get started, and a lot of inexperienced people are going to use it to produce a lot of shoddy crap. Anyone relying on ChatGPT that doesn't already know what they're doing is setting themselves up for failure.
> said by someone who uses chatgpt extensively, it is good for the structure, to get an idea, but as a code generator it kinda sucks.
Interestingly, the same applies to text-to-image programs. Once you used these for a while, you realize their utility and value are little more than an inspiration or a starter. Even if you wanted to ignore the ethical implications, very little they produce is useable. LLM are amazing. However, their end-product application is overrated.
I dunno about that. I honestly tried to extract _any_ value in my day to day work from LLMs, but aside from being an OK alternative to Google/SO, I mostly did find it to be a crutch.
I never had issues with quickly writing a draft or typing in code. I do realise that for a lot of people, starting on a green field is hard, but for me it's easier.
My going hypotheses is that people are just different, and some get true value out of it while others don't. If it works for you, I'm not gonna call you names for it.
Yes, try it with a proprietary language, a closed source environment and lots of domain and application knowledge required to achieve anything. There ChatGPT is completely out of it.
Here is a bunch of JSON. Output the c# classes that can deserialise it.
An intern in college could do that, but it isn’t worth our time to do.
For this function, write the unit tests. Now you do not have anything that you can blindly commit, but you are at the stage where you are reviewing code.
Could you do all of this by hand? Sure but you never would, you would use an IDE. Chatgpt is better than an IDE when you know how to use it.
I think it can be a productivity booster. At my company, I need to touch multiple projects in multiple languages. I can ask ChatGPT how to do something, such as enumerate an array, in a language I’m less familiar with so that I can more quickly contribute to a project. Using ChatGPT as a coach, I am also slowly learning new things.
You mean the search engine that NEVER EVER gives me the documentation I'm locking for, but always goes for a vaguely related blog with hundreds of ads?
Sounds like the perspective of someone who never gave that tool a real chance. To me it’s primarily just another aid like IDE inspections, IDE autocompletions etc.
I use GitHub Autopilot mainly for smaller chunks of code, another kind of autocompletion, which basically safes me from typing, Googling or looking into the docs, and therefore keeps me in the flow.
I'd avoid the use of the word crutch, it sounds ableist as fuck; my girlfriend has a joint disease and relies on a crutch to walk. In other words, while I understand it's just a figure of speech: how dare you.
It's a tool that can help, just like an IDE, code generators, code formatters, etc. No need to talk down on it in that fashion, and there's no need to look down your nose at tools or the people that use it.
How is it offensive to use crutch in this context? The implication being that it's a tool that helps people do something they'd struggle with otherwise. I don't see why anyone might be offended by that.
Your girlfriend is less able to walk and thus uses a crutch to compensate, but it doesn't let her walk as well as a normal person. Replace "walk" with "code" and the sentence works for ChatGPT if the grandparent is correct.
To effectively sue you, I believe the plaintiff would have to prove the LLM you were using was trained on that IP and it was not in the public domain. Neither seems very doable.
I don't actually think either of those things are all that hard, certainly it's a gray area until this actually happens but I think AI generation is not all that different from any other copyright situation. Even with regular copyright cases you don't need to prove "how" the copying occurred to show copyright infringement, rather you just have to show that it's the likely explanation (to the level of some standard). Point being, you potentially don't need to prove anything about the AI training as long as you can show that the AI's result is clearly identifiable as your work and is extremely unlikely to be generated any other way.
Ex. CoPilot can insert whole blocks of code with comments and variable names from copyrighted code, if those aspects are sufficiently unique then it's extremely unlikely to be produced any way other than coming from your code. If the code isn't a perfect copy then it's trickier, but that's also the case if I copy your code and remove all the comments, so it's still not all that different from the current status quo.
The bigger question is who gets sued, but I can't imagine any AI company actually making claims about the copyright status of the output of their AI, so it's probably on you for using it.
It could open quite a wide window for patent trolls though, who generally go for settlements under the threat of a protracted court battle which is of minimal cost to them, as they are often single purpose law firms that do that and only that.
Being able to have your legal counsel tell them to go bug openAI could potentially save you from quite a few anklebiters all seeking to get their own piece.
Your observation highlights the complexities of legal actions related to AI-generated content. Proving the exact source of a specific piece of content from a language model like the one I'm based on can indeed be challenging, especially when considering that training data is a mixture of publicly available information. Additionally, the evolving nature of AI technology and the lack of clear legal precedents in many jurisdictions further complicate the matter. However, legal interpretations may vary, and it's advisable for any legal proceedings to involve legal experts well-versed in both AI technology and intellectual property law. Also, check out AC football cases.
Just curious, do they have bans on "traditional" online sources like Google search results, Wikipedia, and Stack Overflow?
From my view, copying information from Google search results isn't that much different from copying the response from ChatGPT.
Notably Stack Overflow's license is Creative Commons Attribution-ShareAlike, which I believe very people actually realize when copying snippets from there.
> Notably Stack Overflow's license is Creative Commons Attribution-ShareAlike, which I believe very people actually realize when copying snippets from there.
A lot of the snippets would not meet the standard for copyrightable code, though. At least that’s my understanding as non-lawyer.
With SO you also have no guarantee that the person has the license to put that snippet. Even that could have been copied from somewhere else.
A customer was scanning for and banning SO, if that was the only determined source.
> but the other is our using something we are not authorized to use because the tool has it already in its data.
We won't know if this is legally sound until a company who isn't forbidding A.I. usage gets sued and they claim this as a defense. For all we know the court could determine that, as long as the content isn't directly regurgitated, it's seen as fair use of the input data.
So, how do you plan to commercialize your product? I have noticed tons of chatbot cloud-based app providers built on top of ChatGPT API, Azure API (ask users to provide their API key). Enterprises will still be very wary of putting their data on these multi-tenant platforms. I feel that even if there is encryption that's not going to be enough. This screams for virtual private LLM stacks for enterprises (the only way to fully isolate).
We have a cloud offering at https://trypromptly.com. We do offer enterprises the ability to host their own vector database to maintain control of their data. We also support interacting with open source LLMs from the platform. Enterprises can bring up https://github.com/go-skynet/LocalAI, run Llama or others and connect to them from their Promptly LLM apps.
We also provide support and some premium processors for enterprise on-prem deployments.
But, in order to generate the vectors, I understand that it's necessary to use the OpenAI's Embeddings API, which would grant OpenAI access to all client data at the time of vector creation. Is this understanding correct? Or is there a solution for creating high-quality (semantic) embeddings, similar to OpenAI's, but in a private cloud/on premises environment?
Enterprises with Azure contracts are using embeddings endpoint from Azure's OpenAI offering.
It is possible to use llama or bert models to generate embeddings using LocalAI (https://localai.io/features/embeddings/). This is something we are hoping to enable in LLMStack soon.
Enterprises can bring up https://github.com/go-skynet/LocalAI, run Llama or others and connect to them from their Promptly LLM apps - So spin up GPU instances and host whatever model in their VPC and it connects to your SaaS stack? What are they paying you for in this scenario?
Usually that’s not a problem it just means adding OpenAI as a data processor (at least under ISO 27017). There’s a difference between sharing data for commercial purposes (which is usually verboten), vs for data-processing purposes.
I've been maintaining SOC2 certification for multiple years, and I'm here to say that it's largely performative and an ineffective indicator of security posture.
The SOC2 framework is complex and compliance can be expensive. This can lead organizations to focus on ticking the boxes rather than implementing meaningful security controls.
SOC2 is not a good universal metric for understanding an organization's security culture. It's frightening that this is the best we have for now.
Will be doing a show HN for https://proc.gg, a generative AI platform I've built during my sabbatical.
I personally believe that in addition to OpenAI's offering, the ability to swap to an open source model e.g. Llama-2 is the way to go for enterprise offerings in order to get full control.
Azures ridiculous agreement likely put a lot of orgs off. They also shouldn't have tried to "improve" upon OpenAI's APIs. OpenAI's APIs are a little under thought (particularly fine tuning) but so what?
Non-use of enterprise data for training models is table-stakes for enterprise ML products. Google does the same thing, for example.
They'll want to climb the compliance ladder to be considered in more highly-regulated industries. I don't think they're quite HIPAA-compliant yet. The next thing after that is probably in-transit geofencing, so the hardware used by an institution reside in a particular jurisdiction. This stuff seems boring but it's an easy way to scale the addressable market.
Though at this point, they are probably simply supply-limited. Just serving the first wave will keep their capacity at a maximum.
(I do wonder if they'll start offering batch services that can run when the enterprise employees are sleeping...)
Not hi-trust afaik, but they will do hipaa eligible baa with select customers. As sister comment says, it’s easier to go through azure and you get basically the gamut of azure compliance certs for free.
I thought they already didn't use input data from the API to train; that it was only the consumer-facing ChatGPT product from which they'd use the data for training. It is opt-in for contributing inputs via API.
The ChatGPT model has violated pretty much all open source licenses (including MIT license which needs attribution. Show me one single OSS project's license attribution before arguing please.) and is standing still. With the backing of microsoft, I am confused. What will happen if they violate their promise and train data selectively from competitors or potential small companies?
What is actually stopping them? Most companies won't have the fire power to go against microsoft backed openai. How can we ensure that they can't violate this? How can they be practically held accountable?
This as far as I am concerned is "Trust me bro!". How is it not otherwise?
> The ChatGPT model has violated pretty much all open source licenses
Are you claiming this because they used copyrighted material as training data? If so, I think you're starting from the wrong point.
Please correct me if I'm wrong, but last I heard using copyrighted data is pretty murky waters legally and they're operating in a gray area. Additionally, I don't think many open source licenses explicitly forbid using their code as training data. The issue isn't just that most other companies don't have the resources to go up against Microsoft/OpenAI, it's that even if they did, it isn't clear whether the courts would find that Microsoft/OpenAI did anything wrong.
I'm not saying that I side with Microsoft/OpenAI in this debate, but I just don't think this is as clear cut as you're making it seem.
> Are you claiming this because they used copyrighted material as training data? If so, I think you're starting from the wrong point.
All open source license comes under copyright law. It means if they violate the OSS license, the license is void and the tech/material becomes copyright protected. So yes, it would mean that it is trained on copyrighted material.
> Additionally, I don't think many open source licenses explicitly forbid using their code as training data.
It doesn't forbid. For example, permissive license like MIT can be used to train LLM's if they are in compliance. The only requirement when you train on a MIT licensed codebase is that you need to provide attribution. It is one of the easiest license to comply. It means, you just need to copy paste the copyright notice. The below is the MIT license of Emberjs.
Copyright (c) 2011 Yehuda Katz, Tom Dale and Ember.js contributors
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
This copyright notice needs to be somewhere in ChatGPT's website/product to be in compliance with MIT license. If it is not, MIT license is void and you are violating the license. The end result is you are training on copyrighted material. I am more than happy to be corrected if you could find me any single OSS license attribution shown somewhere for training the openai model.
Also, this can be still be fixed by adding the attribution for the code that is trained on. THIS IS MY ARGUMENT. The absolute ignorance and arrogance is their motivation and agenda.
Which is why I am asking, WHAT IS STOPPING THEM FROM VIOLATING THEIR OWN TERMS AND CONDITIONS FOR CHATGPT ENTERPRISE?
First offense could be excused as "blazing a trail and burning down the forest by accident".
But now they have a direct business contract with bigger companies that can lawyer up way better than open source foundations that live on donations and goodwill of code contributors.
Imagine they make a huge deal with Sony or Dell and either company can prove their "secure" enterprise plan was used for corporate espionage.
The legal and reputation repercussion could sink even a fortune 100 company
I thought attribution is required only if you redistribute the code. That’s why saas businesses don’t need to attribute when using open source code on their backend. Maybe a similar concept could be used for training data. I’m far from an expert so this is just a thought.
ChatGPT does redistribute the code, it's essentially the same issue as someone reading proprietary sources or GPL sources on a proprietary project, because they aren't abiding by the license they are breaking the terms. there is no possibility of clean room implementations with ChatGPT
My whole point is that I don't think that's legally true at the moment. There's enough difference in how generative AI works compared to pretty much anything before it that what ChatGPT legally does is up for debate. If a court rules that what ChatGPT does counts as redistribution then yes, I agree that they're likely violating copyright law, but AFAIK that ruling hasn't happened yet.
This is the wrong way to look at it. This comes in the same line of saying "we need a new license for AI" argument. There is nothing stopping an LLM/AI from abiding the license. The OSS license can be used by AI's or LLM's as long as they comply with the terms.
A license exist with terms. You can abide the terms and use it. It doesn't matter whether an AI, a person or an alien from a distant planet is using it. They can follow the terms. This is not a technical challenge but arrogance to abide.
Also, are you saying a model like chatgpt can do so much complex tasks and text processing but can't recognise an OSS license text of 20ish lines?
I am not sure I can agree. What is stopping them from using only permissive license and adding attribution for all the licenses in a single long page? Nothing. This is not a technical issue.
US copyright/IP management is such a shitsh*w. On one hand you can get sued by patent trolls who own the patent for 'button that turns things on' or get your video delisted for recording at a mall where some copyrighted music is playing in the background, on the other hand, you get people arguing that scraping code and websites with proprietary licences is 'fair use'
Taking this from a different perspective, let's say that ChatGPT, CodePilot, or similar service gets trained on Windows source code. Then a WINE developer uses ChatGPT or CodePilot to implement one of the methods. Is WINE then liable for including Windows proprietary source code in their codebase even if they have never seen that code.
The same would apply to any other application. What if company A uses code from company B via ChatGPT/CodePilot because company B's code was used as training data? Imagine a startup database company using Oracle's database code through use of this technology.
And if a proprietary company accidentally uses GPL code through these tools, and the GPL project can prove that use, then the proprietary company will be forced to open source their entire application.
> the proprietary company will be forced to open source their entire application
Top 1 misconception about open source licenses.
GPL doesn't mean if you use the code your entire project will become GPL.
GPL means if you use the code and your project is not GPL-compatible, you are committing copyright infringement. As if you stole proprietary code. If brought to the court, it would be resolved just like other copyright infringement cases.
> What is actually stopping them? Most companies won't have the fire power to go against microsoft backed openai.
Microsoft/Amazon/Google already have competitor's data in their cloud. They could even fake encryption to get all the customer's disk access. Also most employees use google workspace or office 365 cloud to store and share confidential files. How is different with OpenAI that makes it any more worrying?
> For all enterprise customers, it offers:
> Customer prompts and company data are not used for training OpenAI models.
> Unlimited access to advanced data analysis (formerly known as Code Interpreter)
> 32k token context windows for 4x longer inputs, files, or follow-ups
I'd thought all those had been available for non enterprise customers, but maybe I was wrong, or maybe something changed.
" We do not train on your business data or conversations, and our models don’t learn from your usage. ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. "
Which part of that is new, because I was pretty sure they were saying "we do not train on your business data or conversations, and our models don’t learn from your usage" already. Maybe the SOC 2 and encryption is new?
Use a third party interface which uses the API directly like YakGPT or the OpenAI playgrounds, and you can save some costs that way along with a local chat history that’s not shared with OpenAI.
Prior to releasing the chat history "feature" there was an opt-out form that could be submitted, which did not have any impact on the webapp's functionality. I'm not current enough to know if that form 1) ever had any effect, and 2) if a form-submitted opt-out is still valid given they now have the aforementioned in-app feature.
And you’re going to spend the time reviewing every single commit to make sure the dev didn’t sell out without telling anyone? Or risk running a potentially outdated and vulnerable extension?
The obvious point being that the “fork your own code and write your own kernel” attitude is simply unworkable for 99.999% of the population.
If we had to waste that much time re-inventing the loaf of bread, and then making sure that my neighbors didn’t decide to throw some raisins in my loaf, that we never get around to figuring out the next best thing: slicing it.
>" We do not train on your business data or conversations, and our models don’t learn from your usage. ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. "
That's great. But can customer prompts and company data be resold to data brokers?
But, can they provide a comprehensive dump of all data it was trained on that we can examine? Otherwise my company may end up using IP that belongs to someone else.
It's exactly opposite. The entire point of an enterprise option would be that you DO train it on corporate data, securely. So the #1 feature is actually missing, yet is announced as in the works.
Can't believe the pushback I'm getting here. The use case is stunningly obvious.
Companies want to dump all their Excels in it and get insights that no human could produce in any reasonable amount of time.
Companies want to dump a zillion help desk tickets into and gain meaningful insights from it.
Companies want to dump all their Sharepoints and Wikis into it that currently nobody can even find or manage, and finally have functioning knowledge search.
You absolutely want a privately trained company model.
None of the use cases you are describing require training a new model. You really don't want to train a new model, that's not a good way of getting them to learn reliable facts and do so without losing other knowledge. The fine tuning for GPT 3.5 suggests something like under a hundred examples.
What you want is to get an existing model to search a well built index of your data and use that information to reason about things. That way you also always have entirely up to date data.
People aren't missing the use cases you describe, they're disagreeing as to how to achieve those.
>>Companies want to dump all their Excels in it and get insights that no human could produce in any reasonable amount of time.
>>Companies want to dump a zillion help desk tickets into and gain meaningful insights from it.
>>Companies want to dump all their Sharepoints and Wikis into it that currently nobody can even find or manage, and finally have functioning knowledge search.
Mature organizations already have solutions for all of these things. If you can't mine your own data competently, you've got bigger problems than not having AI doing it for you. It means you don't have humans who understand what's going on. AI is not the answer to everything.
I wonder if corporations would train it on emails/Exchange as well, since they are often technically company property and could contain valuable information not found in tickets/wikis.
I think those are examples of prompting, not modeling. You'd use the API to develop an app where the end user's question gets prefaced with that stuff. Modeling is more like teaching it how to sensibly use language, which can be centralized instead of each enterprise having experts in that. It would be like having in-house English teachers instead of sending people to school, based on a desire to have a corporate accent -- interesting but probably not useful in most cases.
ChatGPT doesn’t work for this. There is a huge GIGO problem here that it’s missing the organizational knowledge to disambiguate. Unless you’ve pre-told it which excel sheets are correct, this is DOA.
ChatGPT only works as well as it does because it’s been trained on a corpus of “internet accepted” answers. It can’t fucking reason about raw data. It’s a language model.
But that's exactly the point, an enterprise offering should be able to provide guarantees like this while also allowing training - model per tenant. I think the reality is they are doing multi-tenant models which means they have no way guarantee your data won't be leaked unless they disable training altogether.
I'm imagining some corporate scenario where Coca Cola or Pepsi are purposefully training models on poisoned information so they can out each other for trying to use AI services like ChatGPT to glean information about competitors via brute force querying of some type
ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. Our new admin console lets you manage team members easily and offers domain verification, SSO, and usage insights, allowing for large-scale deployment into enterprise.
I think this will have a solid product-market-fit. The product (ChatGPT) was ready but not enterprise. Now it is. They will get a lot of sales leads.
Just the SOC2 bit will generate revenue… If your organization is SOC2 compliant, using other services that are also compliant is a whole lot easier than risking having your SOC2 auditor spend hours digging into their terms and policies.
I believe the API (chat completions) has been private for a while now. ChatGPT (the chat application run by OpenAI on their chat models) has continued to be used for training… I believe this is why it’s such a bargain for consumers. This announcement allows businesses to let employees use ChatGPT with fewer data privacy concerns.
Note that turning 'privacy' on is buried in the UI; turning it off again requires just a single click.
Such dark patterns, plus their involvement in crypto, their shoddy treatment of paying users, their security incidents... make it harder for me to feel good about OpenAI spearheading the introduction of (real) AI into the world today.
> Such dark patterns, plus their involvement in crypto, their shoddy treatment of paying users, their security incidents... make it harder for me to feel good about OpenAI spearheading the introduction of (real) AI into the world today.
Interesting. My opinion is it is a great product that works well for me, I don't find my treatment as a paying user shoddy, and their security incident gives me pause.
> I don't find my treatment as a paying user shoddy
I have never payed for a service with worse uptime in my life than ChatGPT. Why? So that OpenAI could ramp up their user-base of both free and paying users. They knowingly took on far more paying users than they could properly support for months.
There are justifications for the terrible uptime that are perfectly valid, but in the end, a customer-focused company would have issued a refund to the paying customers for the months during which they were shafted by OpenAI prioritizing growth.
That doesn't mean OpenAI isn't terrific in some ways. They're also lousy in others. With so many tech companies, the lousy aspects grow in significance as the years pass. OpenAI, because of all the reasons in my parent comment, is not off to a great start, imo.
That's an important correction. Thanks, I got a bit carried away with the comment. There's enough hearsay on the internet, and I don't want to contribute.
While we're at it, another exaggeration I made is "security incidents"; in fact, I am only aware of one.
If you turn off history and training, you as the user can no longer see your history, and OpenAI won't train with your data. But can customer prompts and company data still be resold to data brokers?
Worse (IMO) than that is the fact that when the privacy mode is turned on, you can't access your previously saved conversations nor will it save anything you do while it's enabled. Really shitty behaviour.
What about prompt input and response output retention for x days for abuse monitoring? does it not do that for enterprise? For Microsoft Azure's OpenAI service, you have to get a waiver to ensure that nothing is retained.
I'm going to see if the word "Enterprise" convinces my organization to allow us to use ChatGPT with our actual codebase, which is currently against our rules.
- GPT-4 API: has max 8K tokens (for most users atm)
- GPT-3.5 API: has max 16K tokens
I'd consider the 32K GPT-4 context the most valuable feature. In my opinion OpenAI shouldn't discriminate in favor of large enterprises. It should be equaly available to normal (paying) customers.
Thanks. I'm currently using the API models (even GPT 3.5 16K) for things that require a larger context. So much for "Priority access to new features and improvements" as advertised with Plus.
Having conversations saved to go back to like in the default setting on Pro, that's disabled when a Pro user turns on the privacy setting, is another big difference.
This assumes the portion of the enterprise fee related to this feature is only large enough to cover the cost of losing potential training data, which is an absurd assumption that can't be proven and has no basis in economic theory.
Companies are trying to maximize profit; they are not trying to minimize costs so they can continue to do you favors.
These arguments creep up frequently on HN: "This company is doing X to their customers to offset their costs." No, they are a company, and they are trying to make money.
The fact that companies want to maximise profits doesn't prove the point you think it does.
Nobody is arguing that there's an exact matching of value to the company between 1 user giving OpenAI permission to use their chat history for future training and 1 user paying $20/month. But based on your simplistic view, no company would ever offer a free tier because it's not directly maximising revenue.
It's very obvious that getting lots of real-world examples of users using ChatGPT is beneficial for multiple reasons - from using in future training runs (or fine tuning), to analysing what users what to use LLMs for, to analysing what areas ChatGPT is currently performing well or badly in, etc.
So it's not about blankly and entirely "offsetting costs", it's about the fact that both money into their bank account and this sort of data into their databases are both beneficial to the long-term profitability of the company even though only one of them is direct and instant revenue.
Before ChatGPT was released for the world to use, OpenAI were even paying people (both employees and not) to have lots of conversations with it for them to analyse. The exact same logic that justified that justifies allowing some users to pay some or all of the fee for the service in data permissions rather than money.
I'm speaking from experience making these sorts of business decisions, and to a company like OpenAI this is just basic common sense.
Interesting, but I am a bit disappointed that this release doesn't include fine-tuning on an enterprise corpus of documents. This only looks like a slightly more convenient and privacy-friendly version of ChatGPT. Or am I missing something?
At the bottom, in their coming soon section: "Customization: Securely extend ChatGPT’s knowledge with your company data by connecting the applications you already use"
On the other hand, there was a lot of knowledge in those documents that effectively got lost - while the relevant tech is still underpinning half the world. For example: DCOM/COM+.
I saw it, but it only mentions "applications" (whatever that means) and not bare documents. Does this mean companies might be able to upload, say, PDFs, and fine-tune the model on that?
Pretty unlikely. Generally you don't use fine-tuning for bare documents. You use retrieval augmented generation, which usually involves vector similarity search.
Fine-tuning isn't great at learning knowledge. It's good at adopting tone or format. For example, a chirpy helper bot, or a bot that outputs specifically formatted JSON.
I also doubt they're going to have a great system for fine-tuning. Successful fine-tuning requires some thought into what the data looks like (bare docs won't work), at which point you have technical people working on the project anyway.
Their future connection system will probably be in the format of API prompts to request data from an enterprise system using their existing function fine-tuning feature. They tried this already with plugins, and they didn't work very well. Maybe they'll come up with a better system. Generally this works better if you write your own simple API for it to interface with which does a lot of the heavy lifting to interface with the actual enterprise systems, so the AI doesn't output garbled API requests so much.
When I first started working with GPT I was disappointed in this. I thought like the previous commentor that I could fine tune by adding documents and it would add it to the "knowledge" of GPT. Instead I had to do what you suggest is vector similarity search, and add the relevant text to the prompt.
I do think an open line of research is some way for users to just add arbitrary docs in an easy way to the LLM.
Yes, this would definitely be a game changer for almost all companies. Considering how huge the market is, I guess it's pretty difficult to do, or it would be done already.
I certainly don't expect a nice drag-and-drop interface to put my Office files and then ask questions about it coming in 2023. Maybe 2024?
That would be the absolute game-changer. Something with the "intelligence" of GPT-4, but it knows the contents of all your stuff - your documents, project tracker, emails, calendar, etc.
Unfortunately even if we do get this, I expect there will be significant ecosystem lock-in. Like, I imagine Microsoft is aiming for something like this, but you'd need to use all their stuff.
There are great tools that do this already in a support-multiple-ecosystems kind of way! I'm actually the CEO of one of those tools: Credal.ai - which lets you point-and-click connect accounts like O365, Google Workspace, Slack, Confluence, e.t.c, and then you can use OpenAI, Anthropic etc to chat/slack/teams/build apps drawing on that contextual knowledge: all in a SOC 2 compliant way. It does use a Retrieval-Augmented-Generation approach (rather than fine tuning), but the core reason for that is just that this tends to actually offer better results for end users than fine tuning on the corpus of documents anyway!
Link: https://www.credal.ai/
What are the limitations on adding documents to your system? Your website doesn't particularly highlight that feature set, which it probably should if you support it!
Thanks for the feedback! Going to make some changes to the website to reflect that later today! Right now we support connecting Google Doc, Google Sheet, PDFs from Google Drive, Slack channel, or Confluence space. O365, Notion and a couple other sources integrations are in beta. We don't technically have restrictions on volume, the biggest customers we have have around 100 GB of data with us total. If you were trying to connect a terrabyte worth of data, that might be a conversation about pricing! :)
Your pricing seems to eliminate some use cases, including mine.
Rather than wanting to import N documents per month, I would want to import M documents all at once, then use that set of documents until at some future time I want to import another batch of K documents (probably a lot smaller than M) or just one document once in a while.
By limiting it to a fixed amount of documents per month, it eliminates all the applications where you need to import a complete corpus before the service is useful.
Totally agree. retrieval augmented generation is still the preferred way to give the LLM more knowledge. Fine-tuning is mostly useful for adapting the base model for another task. I wrote about this in a recent blog post: https://vectara.com/fine-tuning-vs-grounded-generation/.
Anyone knows how this new capability works in terms of where the model inference be done? Would it still be at the OpenAI side or is this going to be at the customer side?
After using RAG with pgvector for the last few months with temperature 0, it's been pretty great with very little hallucination.
The small context window is the limiting factor.
In principle, I don't see the difference between a bunch of fine-tuned prompts along the lines of "here is another context section: <~4k-n tokens of the corpus>", which is the same as what it looks like in a RAG prompt anyway.
Maybe the distinction of whether it is for "tone" or "context" is based on the role of the given prompts and not restricted by the fine-tuning process itself?
In theory, fine-tuning it on ~100k tokens like that would allow for better inference, even with the RAG prompt that includes a few sections from the same corpus. It would prevent issues where the vector search results are too thin despite their high similarity. E.g. picking out one or two sections of a book which is actually really long.
For example, I've seen some folks use arbitrary chunking of tokens in batches of 1k or so as an easy config for implementation, but that totally breaks the semantic meaning of longer paragraphs, and those paragraphs might not come back grouped together from the vector search. My approach there has been manual curation of sections allowing variations from 50 to 3k tokens to get the chunks to be more natural. It has worked well but I could still see having the whole corpus fine-tuned as extra insurance against losing context.
It's not impossible that fine-tuning would also help RAG. but it's certainly not guaranteed and hard to control. Fine-tuning essentially changes the weights of the model, and might result in other, potentially negative outcome, like loss of other knowledge of capabilities of the resulting fine-tuned LLM.
Other considerations:
(A) would you fine-tune daily? weekly? as data changes?
(B) Cost and availability of GPUs (there's a current shortage)
My experience is that RAG is the way to go, at least right now.
But you have to make sure your retrieval engine work optimally: getting the very most relevant pieces of text from your data: (1) using a good chunking strategy that's better than arbitrary 1K or 2K chars (2) using a good embedding model (3) Using hybrid search, and a few other things like that.
Certainly the availability of longer sequence models is a big help
Yeah, I'll be curious to see what it means by this. Could be a few things, I think:
- Codebases
- Documents (by way of connection to your Box/SharePoint/GSuite account)
- Knowledgebases (I'm thinking of something like a Notion here)
I'm really looking forward to seeing what they come up with here, as I think this is a truly killer use case that will push LLMs into mainstream enterprise usage. My company uses Notion and has an enormous amount of information on there. If I could ask it things like "Which customer is integrated with tool X" (we keep a record of this on the customer page in Notion) and get a correct response, that would be immensely helpful to me. Similar with connecting a support person to a knowledgebase of answers that becomes incredibly easy to search.
Azure-hosted GPT already lets you "upload your own documents" in their playground; it seems to be similar to how ChatGPT GPT-4 Code Interpreter handles file uploads.
You don't fine-tune on a corpus of documents to give the model knowledge, you use retrieval.
They support uploading documents to it for that via that code interpreter, and they're adding connectors to applications where the documents live, not sure what more you're expecting.
Yes, but what if they are very large documents that exceed the maximum context size, say, a 200-page PDF? In that case won't you be forced to do some form of fine-tuning, in order to avoid a very slow/computationally expensive on-the-fly retrieval?
Fine-tuning the LLM in the way that you're mentioning is not even an option: as a practical rule fine-tuning the LLM will let you do style transfer, but you knowledge recall won't improve (there are edge cases, but none apply to using ChatGPT)
That being said you can use fine tuning to improve retrieval, which indirectly improves recall. You can do things like fine tune the model you're getting embeddings from, fine tune the LLM to craft queries that better match a domain specific format, etc.
It won't replace the expensive on-the-fly retrieval but it will let you be more accurate in your replies.
Also retrieval can be infinitely faster than inference depending on the domain. In well defined domains you can run old school full text search and leverage the LLMs skill at crafting well thought out queries. In that case that runs at the speed of your I/O.
We have >200 page PDFs at https://docalysis.com/ and there's on-the-fly retrieval. It's not more computationally expensive than something like searching one's inbox (I'd image you have more than 200 pages worth of emails in your inbox).
Haha i also thought about that Y Combinator video. Yep, their prediction didn't age well and it's becoming clear that openAI is actually a direct competitor to most of the startups that are using their api. Most "chat your own data" startups will be killed by this move.
No different than Apple, then. A lot of value is provided to customers by providing these features through a stable organization not likely to shutter within 6 months, like these startup "ChatGPT Wrappers". I hope that they are able to make a respectable sum and pivot.
I think almost each startup is focusing on enterprise as it sounds lucrative but selling to an enterprise might qualitatively offset its benefits in some way (very painful).
Personally I love what Evenup Law is doing. Basically find a segment of the market that runs like small businesses and that has a lot of repetitive tasks they have to do themselves and go to them. Though I can't really think of other segments like this :)
If your entire startup was just providing a UI on top of the ChatGPT API, it probably wasn't that valuable to begin with and shutting it down won't be a meaningful loss to the industry overall.
There's a typical presumed business intuition that any large company will confer business to a host of "satellite companies" who offer some offshoot of the product's value proposition but catered to a niche sector. Most of these are however just "OpenAI API + a prefix prompt + user interface + marketing". The issue is (which has been brought up since the release of the GPT-3 API 3 years ago) that no startup can offer much more value than the API alone offers, and thus it's easier, comparatively, than in analogous cases of this type of startup model with other larger companies in the past, for OpenAI to capitalize on this business
This has been the weirdest part of the current wave of AI hype, the idea that you can build some kind of real business on top of somebody else's tech which is doing 99.9% of the work. There are hard limits on how much value you can add.
If you want to build something uniquely useful, you probably have to do your own training at least.
Any startup that is using ChatGPT under the hood is just doing market research for OpenAI for free. The same happened when people started experimented with GPT3 for code completion, right before being replaced by Copilot.
If you want to build an AI start-up and need a LLM, you must use Llama or another model than you can control and host yourself, anything else is basically suicide.
>Any startup that is using ChatGPT under the hood is just doing market research for OpenAI for free
It's not free if you have paying clients.
> If you want to build an AI start-up and need a LLM, you must use Llama or another model than you can control and host yourself, anything else is basically suicide.
You're still doing market research for OpenAI. Just because you aren't using their model doesn't mean they can't copy your UX. Prompts are not viable trade secrets after all.
No early stage start-up has revenues covering their expenses. But in fact you're right, it's not even “free”, it's “investor-subsidized” market research.
> You're still doing market research for OpenAI. Just because you aren't using their model doesn't mean they can't copy your UX. Prompts are not viable trade secrets after all.
Prompt aren't viable trade secret, but fine-tuning datasets and strategies, customer data[1], customer habits, user feedback, etc. are. And if you're using OpenAI, you're giving all that to them. Also, given their positioning, they cannot address any use-case that involve deploying your model inside your customer's infrastructure, so this kind of market research is completely irrelevant for them.
[1]: And don't get fooled by wordings saying that they don't train on customer data, they are still collecting much more info that what you'd like them to. For instance, even just knowing the context size that users like to work with in different scenario is a really interesting data for them, and you can be sure that they collect it and adapt to.
"Unlimited access to advanced data analysis (formerly known as Code Interpreter)"
Code Interpreter was a pretty bad name (not exactly meaningful to anyone who hasn't studied computer science), but what's the new name? "advanced data analysis" isn't a name, it's a feature in a bullet point.
Also I'd heard anecdotally on the internet (Ethan Mollick's twitter I think) that 'code interpreter' was better than GPT 4 even for tasks that weren't code interpretation. Like it was more like GPT 4.5. Maybe it was an experimental preview and only enterprises are allowed to use it now. I never had access anyway.
I still have access in my $20/m non-Enterprise Pro account, though it has indeed just updated its name from Code Interpreter to Advanced Data Analysis. I haven't personally noticed it being any better than standard GPT4 even for generation of code that can't be run by it (ie non-Python code).
I've been using it heavily for the last week - hopefully it doesn't become enterprise only... it's very convenient to pass it some examples and generate and test functions.
And it does seem "better" than standard 4 for normal tasks
Seemed like a great project. Hope to see it come back!
There are some great open-source projects in this space – not quite the same – many are focused on local LLMs like Llama2 or Code Llama which was released last week:
The UI is relatively mature, as it predates llama. It includes upstream llama.cpp PRs, integrated AI horde support, lots of sampling tuning knobs, easy gpu/cpu offloading, and its basically dependency free.
yes. at first glance it looks like a windows app but it's actually very portable. it has some parameters for gpu offloading and extended context size that just work. it exposes an api endpoint. i use it on a workstation to serve larger llms locally and like the performance and ease of use.
Ollama is very neat. Given how compressible the models are is there any work being done on using them in some kind of compressed format other than reducing the word size?
There are different levels of quantization available for different models (if that's what you mean :). E.g. here are the versions available for Llama 2: https://ollama.ai/library/llama2/tags which go down to 2-bit quantization (which surprisingly still happens to work reasonably well).
No, what I mean is that it seems as though there is quite a bit of sparseness to the matrix and I was wondering if that can somehow be used to further shrink the model, quantization is another effect (it leaves the shape of the various elements as they are but reduces their bit-depth).
Ah, gotcha! I thought you probably meant something else. I've been wondering this too, and it's something I've been meaning to look at.
On a related note it doesn't seem like many local runners are leveraging techniques like PagedAttention yet (see https://vllm.ai/) which is inspired by operating system memory paging to reduce memory requirements for LLMs.
It's not quite what you mentioned, but it might have a similar effect! Would love to know if you've seen other methods that might help reduce memory requirements.. it's one of the largest resource bottlenecks to running LLMs right now!
That's a clever one, I had not seen that yet, thank you.
The hint for me is that the models compress so well, that suggests the information content is much lower than the size of the uncompressed model indicates which is a good reason to investigate which parts of the model are so compressible and why. I haven't looked at the raw data of these models but maybe I'll give it a shot. Sometimes you can learn a lot about the structure (built in or emergent) of data just by staring at the dumps.
That's quite interesting. I hadn't thought of sparsity in the weights as a way to compress models, although this is an obvious opportunity in retrospect! I started doing some digging and found https://github.com/SqueezeAILab/SqueezeLLM, although I'm sure there's newer work on this idea.
All activity stopped a couple of weeks ago. It was extremely active and had close to 5 thousand stars/watch events before it was removed/made private. Unfortunately I never got around to indexing the code. You can find the insights at https://devboard.gitsense.com/microsoft/azurechatgpt
It looks like your account has been using HN primarily (in fact exclusively) for promotion for quite some time. I'm not sure how we didn't notice this before but someone finally complained, and they're right: you can't use HN this way. Note this, from https://news.ycombinator.com/newsguidelines.html: Please don't use HN primarily for promotion. It's ok to post your own stuff part of the time, but the primary use of the site should be for curiosity.
Normally we ban accounts that do nothing but promote their own links, but as you've been an HN member for years, I'm not going to ban you, but please do stop doing this! We want people to use HN to read and post things that they personally find intellectually interesting—not just to promote something.
If I go back far enough (a couple hundred comments are so), it's clear that you used to use HN in the intended spirit, so this should be fairly easy to fix.
If it was transferred, the /microsoft link would have redirected to it. Instead, it's the git commits re-uploaded to another repo - so the commits are the same but it didn't transfer past issues, discussions or PRs https://github.com/matijagrcic/azurechatgpt/pulls?q=
Based on past discussion, my guess is it was removed because the name and description were wildly misleading. People starred it because it was a repo published by Microsoft called "azurechatgpt", but all it contained was a sample frontend UI for a chat bot which could talk to the OpenAI API.
Sell the same product under two brands at the same time?
Optimal business strategy. Makes it look like there's more competition, and changes the decision from "do we use ChatGPT" to "Which GPT vendor do we use?"
Microsoft has a stake in OpenAI but they don't have a controlling interest in it. What they got instead was exclusive access to the models on Azure. So they benefit from OpenAIs success but they benefit more from their own success in the space and in a way they are competitors.
Microsoft is primarily a mid-market company. They definitely sell to enterprise as well, but what makes Microsoft truly great is their ability to sell at enormous scale through a vast network of partners to every SMB in the world.
OpenAI is a tiny company, relative to Microsoft. They can’t afford to build a giant partner network. At best, they can offer a forum-supported set of products for the little guys and a richly supported enterprise suite. But the middle market will be Microsoft’s to own, as they always do.
It’s pretty interesting to see both companies copying each other. Bing Chat has GPT4 with Vision, Chat History and some other goodies whereas OpenAI extends towards B2B.
At my work we've been using Bing Enterprise for a brief while now, which it states is based on ChatGPT4 and has the same promise of keeping data private. From what I've seen, Bing Chat is superior to ChatGPT4 in many respects.
Or maybe they got urged to offset more operational costs - and I would believe that companies already paying for Microsoft things Wille happily pay for OpenAI in addition just to be safe.
It's helpful to think of OpenAI as Microsoft's R&D lab for AI without the political and regulatory burdens that MSR has to abide by. Through that lens, it's really all just the same thing. There is no endgame for OpenAI that doesn't involve being a part of Microsoft.
IIRC it is impossible for OpenAI to become part of Microsoft since the incorporation documents of the for-profit bit of OpenAI prevent anyone from having a majority of the shares (except the non-profit foundation, of course).
Curious what the latency would be using OpenAI service vs using a hosted LLM like Llama2 on premise? GPT4 is slow and given the retrieval step (coming soon) across all of companies corpus' of data, it could be even slower as it is a sequential step. (Asking more as I am curious at this point)
Another question is does the latency even matter? Today, same employees ping their colleagues for answers and wait for hours till get a reply. GPT would be faster (and likely more accurate) in most of those cases.
There is just nothing out there, open source or otherwise, that even comes close to GPT-4. Therefore, the value proposition is clear, this is providing you with access to the SOTA, 2x faster, without restrictions.
I can actually see this saving a lot of time for employees (1-10% maybe?), so the price is most likely calculated on that and a few other factors. I think most big orgs will eat it like cake.
Are there already some profitable businesses using chatgpt i am wondering. To me the tech is really impressive. But what kind of really big commercial product exists at this point? I only know of assistants like copilot or some word assistant. But what else? Isnt this just a temporary bubble?
If you’re asking about consumer facing products, I’m aware of eBay using it to help sellers write product descriptions. But, I think the bigger immediate use case is making daily work easier inside these companies.
I’ve used it extensively to speed up the process of making presentations, drafting emails, naming things, rubber-ducking for coding, etc.
One aspect of working in a big company is figuring out where all the bits of specialized knowledge live, and what the company-specific processes are for getting things done. One use case for an internal chatGPT is essentially a 100% available mentor inside the company.
This is going to make for some highly entertaining post mortems.
"Management believed Jimmy Intern would be fine to deploy Prod Model Sysphus vN+1; their Beginner Acceleration Divison (BAD) Team was eager to show off the new LLM and how quickly it could on-board a new employee. To his credit, Jimmy asked the BAD model the correct questions for the job. That's when the LLM began hallucinating, resulting in the instructions to 'backup the core database' being mixed up with the instructions for 'emergency wipe of sensitive data'. Following the instructions carefully and efficiently, Jimmy successfully wiped out our core database, the backups, and our local tapes overnight."
Yawn. This is probably The hundredth time I’ve seen this scenario trotted out and knowledge base retrieval and interpretation has been solved since before bing chat was on limited sign up.
You don’t even need to fine tune a model to do this, you just give it a search API to your documentation, code and internal messaging history. It pulls up relevant information based on queries it generated from your prompt and then compiles it into a nicely written explanation with hyperlinked sources.
I'm not sure I follow. Knowledge based retrieval of what exactly? Outdated docs and dilapidated code? Aging internal wikis and chat histories erased for legal reasons?
Everyone also seems to overlook how much time and resources it takes for these models to be trained/fine tuned on a corpus of knowledge. Researches have calculated it probably took OpenAI the equivalent of 355 years on a single NVidia V100 to train up GPT 3. [1] Clearly they used more horsepower in parallel, which is a foreseen problem right now for other reasons. [2]
Clicked on ChatGPT / Compare ChatGPT plans / Enterprise ...
> Contact sales
Oops. Scary.
I'm missing the Teams plan: transparent pricing with a common admin console for our team. Yes, fast GPT-4, 32k context, templates, API credits... they're all very nice-to-haves, but just the common company console would be crucial for onboarding and scaling-up our team and needs without the big-bang "enter-pricey" stuff.
Any "Contact sales" stuff has just been an instant "no" at any company I've ever worked at, because that always means that the numbers are always too high to include in the budget unless it's a directive coming down directly from the top.
I don't know if that's a smart way to bypass pesky hidden information negotiations and suss out other party's upper bound or a really stupid way to do business...
Of course, I'd rather have 20K per customer
But an initial quote of 300K would likely lead to many instant rejections rather than engaging in negotiation, right? That's why I say it feels like a stupid practice, even though it could pay off really well if some company accepts outright (With the caveat that I've never been near this kind of business deal, so I'm just going off of common sense)
That’s where directives for enterprise contracts usually come from. I’m sure they won’t even talk to anyone not willing to pay $100k+ per year. Salesforce’s AI Cloud starts at $365k a year.
> I’m sure they won’t even talk to anyone not willing to pay $100k+ per year.
Wouldn't surprise me. We had a vendor whose product we had used at relatively reasonable rates for multiple years suddenly have a pricing model change. It would have seen our cost go from $10k/yr to $100k/yr. As a small nonprofit we tried to engage them in any sort of negotiation but the response was essentially a curt "too bad." Luckily a different vendor with a similar product was more than happy to take our $10k.
We have a model that I see a lot of others do, even if they don't publicize. We have free, OSS, and cheap SaaS tiers fine for many of our academic users, and when a small group really wants the full enterprise version, we generally offer a heavily discounted pricing model to make that affordable too. The only exception here is when it is a true enterprise sale like a shared resource for a large number of users, and we'd still have to think there too.
The reason is it keeps their low budgets and thus their ROI in alignment, which is why this is pretty normal. So again, I'd recommend asking and just clarifying your are a NGO/EDU. No 100% guarantee, but should be common.
It depends on the company. It's kind of like a menu item that says "Market Price". You know it's not going to be cheap. You don't know until you ask if the price is less than the value offered.
The jump to enterprise pricing suggests that they have enormous enterprise demand and don’t need to bother with SMB “teams” pricing. I suspect OpenAI is leaving the SMB part up to Microsoft to figure out, since that’s Microsoft’s forte through their enormous partner program.
It makes it impossible to access for bootstrapping, at least for people who have budget constraints. Which is just reality, it's a scarce resource and I appreciate what they have made available so far inexpensively.
But hopefully it does give a little more motivation to all of the other great work going on with open models to keep trying to catch up.
Has there been some resolution to the copyright issues that I’m no aware of? In my conversations with execs that’s been a serious concern — basically that generated output from AI systems can’t be reliably protected.
I refer to the concept that output could be deemed free of copyright because they are not created by a human author, or that derivative works can be potential liabilities because they resemble works that were used for training data or whatnot (and we have no idea what was really used to train).
I think they're basically taking the "Uber" strategy here: primary business is probably illegal, but if they do it hard enough and at enough scale and create enough value for enough big companies, then they become big enough to either get regulations changed or strong enough to weather lawsuits and prosecutions. Their copyright fig leaf is perhaps analogous to Uber's "it's not a taxi service so we don't need taxi medallions" fig leaf.
Might be closer to the “Google” strategy, as Google also faced significant litigation with image search and publishers did a ton to shut down their large investment in Google Books. Moreover, Uber flaunted their non-compliance in contrast to sama testifying before Congress and trying to initiate regulatory capture early.
There’s undeniably similar amounts of greed, although TK seems to genuinely enjoy being a bully versus sama is more of a futurist.
This decision seems specifically about whether the ai itself can hold the copyright as work for hire, not whether output generated by ML models can be copyrighted.
You're referring to a Copyright Office administrative ruling.
It's a pretty strange ruling at odds with precedent, and it has not been tested in court.
Traditionally all that's required for copyrightability is a "minimal creative spark", i.e. the barest evidence that some human creativity was involved in creating the work. There really hasn't traditionally been any lower bound on how "minimal" the "spark" need be -- flick a dot of paint at a canvas, snap a photo without looking at what you're photographing, it doesn't matter as long as a human initiated the work somehow.
However, the Copyright Office contends that AI-generated text and images do not contain a minimal creative spark:
This is obviously asinine. Typing in "a smiling dolphin" on Midjourney and getting an image of a smiling dolphin is clearly not a program "operat[ing] randomly or automatically without any creative input or intervention from a human author".
If our laws have meaning, it will be overruled in court.
Of course, judges are also susceptible to the marketing-driven idea that Artificial Intelligence is a separate being, a translucent stock photo robot with glowing blue wiring that thinks up ideas independently instead of software you must run with a creative input. So there's no guarantee sanity will prevail.
Not so much copyrighting generated output, it’s more about to what extent training is fair use as well as when the algo spits out an exact copy of training data.
If the model produces content that would be a copyright violation in any other context, it doesn't stop being a copyright violation regardless of any of these decisions. No one has ever disagreed with that; it's the functional abolition of copyright and if you're okay with that, then you're not arguing for this stuff, you're arguing for the abolition of copyright.
The argument is that if you trained the model with copyrighted data, and then you or someone else separately used the model to generate novel media which was not legally similar enough to any copyrighted work to make it a copyright violation, that that isn't violating copyright, it's fair use. Basically, using it to make your own original content is legal, and using it to create an unauthorised reproduction of a copyrighted work is illegal. Just like all other software.
Copyright is only an issue for creative works. If a company is automating a customer service chat or an online ordering process or a random marketing page/PR announcement or something of that sort via ChatGPT why would they even care?
If the code that implements the automation resembles too closely copyrighted code it violates the rights of the creator. But who would know what happens behind corporate walls.
We know that stuff generated from AI content is generally not your copyright, but there isn't any current ruling on whether or not you're free to use copyright-protected content to train a model in a legal way (e.g. fair use since it's been 'learned on', not directly copied). Some companies are using OpenAI gpt stuff, which is almost certainly trained on tons of copyrighted content and academic content, while other companies are being more cautious and soliciting models specifically trained on public domain/licensed content.
If something is in the public domain, and you create something new with it, you have the rights to the new work, there isn’t any sort of alike-licensing on public domain works in most-if-not-all jurisdictions.
This is why music engravers can sell entire books of classical sheet music from *public domain* works. They become the owners of that specific expression. (Their arrangement, font choice, page layout, etc)
If the AI content is public domain, and the work it generates is incorporated into some other work, the entity doing the incorporation owns the work. It’s not permanently tainted or something as far as I know.
The copyright ruling that you are referencing is being significantly misunderstood.
The only think that the ruling said is basically that the most low effort version of AI does not have copyright protection.
IE, if you just go into midjourney and type in "super cool anime girl!" and thats it, the results are not protected.
But there is so much more you can do. For example, you can generate an image, and then change it. The resulting images would be protected due to you adding the human input to it.
If you are concerned somebody will steal your IP or infringe your copyright, they first have to be 100% sure that some text was indeed written by an AI, and only an AI.
In practice, if you suspect something was written by an AI and are considering copying it, you would be safer to just ask an AI to write you one as well.
There have been a few things that have been in the back of my mind that have been painfully bothering me for quite while regarding this subject.
Are people really that different from an AI model when it comes to generating works?
Isn't the underlying and overarching concept and process similar when it comes to this? (Optimizing for a certain select scenario or outcome by iteratively going through ideas and generating more over time.)
What happens when both the model and human converge and it becomes truly indecipherable when it comes to telling the difference?
So, the point that AI output might be a derivative work of its input is finally dead? I thought what execs were really afraid of was the risk that copyright holders will come around after some time and claim rights on the AI output even if it is only vaguely similar to the copyrighted work.
We (like many other companies) have deployed an internal UI[1] that integrates with our SSO and makes calls via the OpenAI API, which has better data privacy terms than the ChatGPT website.
We'd be potentially very interested in an official internal-facing ChatGPT, with a caution that the economics of the consumption-based model have so far been advantageous to us, rather than a flat fee per user per month. I can say that based on current usage, we are not spending anywhere close to $20 per user per month across all of our staff.
can you briefly explain the vulnerability here. I'm having difficulty understanding as all I see is him recalling already previously recorded chat history of his own session. thanks.
The vulnerability is the automatic insecure rendering of image markdown. One way to trigger it is with an indirect prompt injection payload. The scenario is that the user analyzes some text/data, which contains malicious instructions. The owner of the text doesn't have access to the chat history (it's just some random text somewhere), it could be a comment on a webpage, text inside a pdf file, copy/pasting, or even instructions hidden inside an image the user analyzes and sends to the LLM. You can find many examples of indirect prompt injections on my blog (e.g. analyzing YouTube transcripts,...). Just yesterday I put up a video explaining the various TTPs (and also fixes companies put in place): https://www.youtube.com/watch?v=L_1plTXF-FE Hope that helps.
Do you get uncensored answers with this? Oftentimes it produces false positives for my workload, and i dont care for feedback buttons during work hours.
What I don't fundamentally understand is that ChatGPT even 4 just functions as a better Google.
It seems like there's far more ways for extensive automation by decision making via ChatGPT to cause problems than solve problems. With my experience using it for programming, anything that is non-trivial basically requires you to do it all yourself because ChatGPT will hopelessly get it wrong.
True you can’t just assign it a complex task and expect a perfect result in the first answer but using it like Google e.g. one query and one response is not the best way to use it. It’s a chat bot, it’s been optimized for multi turn conversations so use it that way. Pretend you are messaging someone on slack, discuss the issue with them, explain things, ask for feedback, point out mistakes etc. For more advanced use leverage its initial training as a text completion engine and give it several examples of prompt and response up front or write the first part and ask it to complete the rest.
If your company illegally treads on my IP, I don't care if your employees used LLM's or not; I will sue.
By the way, I don't have to win a lawsuit to get some justice; I'll make discovery hurt.
So if OpenAI stole my IP and used it for training (which they probably did, illegally IMO), I guess you're taking that risk if you let your employees use their LLM's.
> If your company illegally treads on my IP, I don't care if your employees used LLM's or not; I will sue.
In the US the saying is "This case will come down to who has the last dime and it won't be you".
> By the way, I don't have to win a lawsuit to get some justice; I'll make discovery hurt.
Even for discovery a common tactic deployed for discovery is to "bury" the other party in discovery materials to the point where they will incur substantial costs just to parse the absurd mountains of materials they send you. Another "bleed you out" strategy.
> And yes, I know that money wins in legal battles. That's why I am focusing on discovery before I don't have any.
When they send you several thousand pages (minimum) of random documents you're looking for a smoking gun that's a needle in a haystack.
That takes money and time that very few parties have, coming back to the last dime expression.
I don't know if you've ever deployed your strategy successfully but needless to say I wouldn't count on it as a viable path to protect your IP against companies valued in the tens of billions of dollars.
Any hot takes on what the median application of this looks like at a practical level? What springs to mind for me is replacing the classic "weed-out" tiers of customer service like phone trees, chatbots that are actually crappy FAQ/KB search engines, and human representatives who are only allowed to follow a script. On balance, this might even be a win for everyone involved, given how profoundly terrible the status quo is. While it's sort of terrifying at a philosophical level that we might be mollifying the masses with an elaborate illusion, the perception of engaging with an agent that's actually responsive to your words might make the whole process at least incrementally less hellish.
I don't intend at all to suggest that the loss of call center jobs is good per se, but rather that the job they're tasked with is a net harm to just about everyone downhill of the owners and C-suite. For what it's worth, I spent a few years riding a bus route that probably would have been cut if it didn't serve these folks, and I've spent almost as many years believing that they deserve better (even though two or three of them had a little bit of fun annoying the nerdy white guy working his way through the Zenithia Trilogy on his 3DS).
Given our industry's long history of lying about data retention and usage and openai's opaqueness and Sam Altman's specific sleaziness I wouldn't trust this privacy statement one bit. But I know the statement will be enough for corporate "due diligence".
Which is a shame because an actual audit of the live training data of these systems could be possible, albeit imperfect. Setup an independent third party audit firm that gets daily access to a randomly chosen slice of the training data and check its source. Something along those lines would give some actual teeth to these statements about data privacy or data segmentation.
I hope no one will be disappointed, who has done a good job as an employee suddenly finding himself replaced by a snippet of code, after the weights have been sufficiently adjusted.
It's like slurping the very last capital a worker has out of its mind and soul. Most companies exist to make a profit, not to employ humans.
Paired with the pseudo-mocked-tech-bro self-peddling BS this announcement reads like dystopia to me. Not that technological progress is bad, but technology should empower users (for real, by giving them more control) not increase power imbalances.
Let's see how many people who cheered today will cheer just as happily in 2028. My bet: just a few.
A common retort to this is that companies also exist to compete (and thus make a profit), so those that use AI to augment their staff rather than replace them will be at an advantage.
Honestly, I can see it, but there are definitely SOME jobs at risk, and it will almost certainly reduce hiring in junior positions.
I am a manager in a dev team. I have a small team and too many plates spinning, and I’ve been crying out for more hires for years.
I moved to using AI a lot more. ChatGPT and Copilot for general dev stuff, and I’m experimenting with local llama-based models too. It’s not that Im getting these things to fill any one role, but to reduce the burden on the roles we have. Honestly, as things stand, I’m not crying out for more hires any more.
Few new hires across the board will mean fewer juniors will get their foot in the door and get enough experience to become seniors. All good for those already well up the ladder, but for those below it feels like the ladder has been pulled up out of their reach
An LLM that’s good enough to replace a junior dev is also good enough to train someone to be as competent as a junior dev. Even better, competent programmers and technically minded entrepreneurs can now afford to “hire” junior dev level LLMs to help them complete their projects and achieve their goals. Further, it isn’t limited to programming but also encompasses every other job that can be done through writing. Every inch of ladder that is lost in corporate employment is paid back in feet of self employment.
I'm all for making us all more efficient, but not at the cost of creating new data monopolies, if possible. The price is very high, even though it's not immediately obvious.
We already have enormous concentration of data in a few places and it's only getting worse. Centralization is efficiency, but the benefits of that get skimmed disproportionally, to the detriment of what allowed these systems to emerge in the first place: our society.
Absolutely right. We like to focus on how GenAI impacts developers here, for obvious reasons. I don't really see developers being impacted in the near-term, but the reality is that there are a lot of jobs that absolutely can go away soon, if we choose to go down that path. I wish we'd talk about those jobs more.
OpenAI loves to talk about a utopia where no one has to work and everyone is paid in Worldcoin (which of course Altman will make a handsome profit off of), but does anyone actually think that GPT-X is leading to this? Some of our most vulnerable members of society will soon find themselves without work, and no easy way to get new work. We don't need to wonder how we as a society will take care of them - all historic evidence points to us doing absolutely nothing.
That was quick. Companies offering APIs end up competing with their developer base that built end-user facing products. Another example is Twilio that offers retail-ready products now such as Studio, prebuilt Flex, etc.
Interesting that they offer GPT-4 32k in the enterprise version while only giving very few people API access to it. I guess we'll see that more often in the future.
They do. If you use the entire context, a single request is like 30 cents. Very easy to rack up 10s of dollars very very quickly. Not an explanation/excuse, but additional context (no pun intended).
This is a really interesting move — as others have been saying in the comments I wonder how AI startups will react going forward.
I bet there will be a lot more effort to build truly open-source models. Also, I wonder why no foundation has yet got involved to pool resources and create large enough models.
Meta is inching towards to that direction through llama series as Google did it through Kubernetes. It'll happen once the dusts settle.
I'm very impressed by how Meta position itself as "AI for rest of us" position through llama and to an extent PyTorch, although I have no idea how they are going to capitalize that position besides hiring. (vs. Google having a cloud offering.)
It's 700 million monthly active users on the LLaMA 2 release date; so if you reach and exceed a significant number of users in the future, you don't lose your license.
As a purist do I wish it was fully open source? Yes. Is it restrictive to "the rest of us"? No.
Finally, if you have more than 700M MAUs, you probably have an internal LLM you should be using.
I hereby dub this "Baron-von-Munchausen-as-a-Service."
Now you can pay real money for a chatbot to make stuff up about your company and its products.
While it is pretty incredible stuff, until or unless they have a veracity bit — some sort of "please don't lie" flag in it, I'd be wary of what it produces.
Will ChatGPT offer off-the-cuff pricing, discounts, rebates and refunds that are in line with your actual business model? Will it make written offers you will be legally-obligated to adhere to? Will it lie about your features and capabilities, or that of your competitors? Will it invent whole-cloth SLAs and KPIs you'll never be able to adhere to? Will it make up arrival and departure times? Medical conditions and prescribed treatments/therapies? Will it glibly give instructions that can get consumers/users who read these statements fired from their jobs, fined, imprisoned or killed?
There are places where you only want to give a generative AI so much room to color outside the lines. And after that, you are going to want traditional procedural logic to take over. Decision-trees from manually-maintained systems and even multi-decadal-honed expert systems.
There are some things where letting a computer make up a plausible response is ... okay-ish. And there are times where you can cause immense amounts of damage by letting things get outside of your control and human review.
This should come with a consumer warning label that would put your typical pharmaceutical safety disclosure to shame.
Now, with all that said, I am amazed at many of the results these LLM-based systems can generate. My main concern is that they are getting so good — or at least so plausible — that they can even fool domain experts unless they read really, really closely. And the experts won't be able to do that at scales of 10s or 100s of thousands of transactions per second.
I just asked ChatGPT to give me directions to my favorite shop in Manhattan from Penn Station. It gave me wrong public transit directions about which subway to take. It also gave me wrong walking directions, putting me blocks off course and as to which side of the street I'd find the shop on.
The only thing it got right was: "Please note that subway schedules and routes can vary, so it's a good idea to use a navigation app like Google Maps or the official MTA website for real-time information and step-by-step directions."
I absolutely agree, and I've been in several similar situations. Yesterday I finally caved and gave it very clear instructions on how I wanted it to build my new IKEA shelf, even though understanding the instructions is most of the work, and guess what? The box is still sitting there, unopened! It hasn't even started. I asked it if it could fight Malenia for me, same thing, more useless machine babble. Repair the slipping rubber on my office chair, incoherent results and not even an attempt to start. Factorise a prime number (I just wanted to break the encryption on some traffic I got a hold of) and it didn't work, again. Just now, I asked ChatGPT what number I was thinking of and it got it so unbelievably wrong... I can't even believe people are using this crap. ChatGPT has very much not come good on the promises OpenAI made about what it could do.
This is kind of like if a fisherman walking down the street looked up at a steel sky scraper and proclaimed:
"Gee, how can they be making entire buildings out of that?! Why my plastic dinghy could be out at sea for a year and not pick up a lick of rust, meanwhile my steel fishing hooks are rusty if I leave them out on a rainy night!"
I'm really unsure if you are making a joke, but in case you're serious: using a language model for directions is like using a thermometer for calculations. Sure, it uses numbers...
The problem is that actual people are trusting the output they get, not realizing that it's just plausible sounding, not correct. It is surprising just how plausible it sounds. Which is the uncanny valley into which any number of people will walk off the proverbial cliff and fall into. The hype surrounding the capabilities of LLMs far exceeds its actual output.
> Also I think you need to better understand how much hallucination has been driven down recently and its clear path going forward.
Unless it’s essentially fully eliminated, it doesn’t qualitatively change the argument. The output cannot be trusted to go directly to customers and/or business partners. It can’t even be trusted to go to employees that don’t know it’s from AI and might lie if you ask if something strange.
“This isn’t fixable,” said Emily Bender, a linguistics professor and director of the University of Washington’s Computational Linguistics Laboratory. “It’s inherent in the mismatch between the technology and the proposed use cases.”
Bender also recently said in some podcast that there's basically no useful case for LLMs. I think she's a good candidate for some soundbite to add to "a global market of maybe five computers", "nobody needs more than 640k", etc (but she is probably not notable enough even for that).
In such cases, I believe there are (and should be) guardrails. It already has some guardrails internally set:
ChatGPT already knows how to say: "I am not a lawyer, but I can provide you with some general information on this topic." The same thing for medicine: "I'm not a medical professional, but I can provide some general information..."
But, for instance, ask it to design a canard for a 4th generation supersonic fighter, and suddenly it spits out pages of output. [Though it finally corrects itself and balks to answer when you ask for angle of forward sweep and a formula for the curvature of the surface.]
There needs to be a way to "sniff out" if there's topics that people are getting too close to danger zones for it to answer. Ways for organizations themselves to set those 'bounding boxes.'
> There needs to be a way to "sniff out" if there's topics that people are getting too close to danger zones for it to answer. Ways for organizations themselves to set those 'bounding boxes.'
I wonder if this can be achieved with the "Custom Instructions" feature. With enterprise, these can be managed by the admin. Could tell ChatGPT something along the lines of:
"Make sure to state you are not an expert if you are asked to comment on any of the following: []"
Azure already has this, there's a literal checkbox that forces it to only answer questions it can cite from your internal documents.
The reality is chat is a terrible interface in the long run. Horrible discoverability, completely non-obvious edges, turns what you might think is an equally accessible tool into the worst case of "you're holding it wrong you've ever seen just judging by what people in these comments are complaining about.
But chat was/is brilliant at making this stuff accessible. I've gone back and learned of incredible things I could have been doing years ago if I had paid more attention to ML, but it wasn't until chat gave us this perfect interface to start from that I suddenly got the spark.
As chat models get more powerful, chat will be the least interesting thing you can do with them. If the model is intelligent enough to convert minimal instructions into complex workflows, why do I need to chat with it? To take it to its logical extreme, why not have one button that just does the right thing?
The more realistic version of that will be industry specific interfaces that focus on the 5 buttons you need to get your job done, along with the X decades of procedure that can guide the LLM to exactly what needs to be done.
One of the bounding boxes should be requests to introspect. The models absolutely are incapable of introspection. They just simulate a human introspecting, but they think totally differently from a human, so the output is less than useless. If you ask “why did you say X” it should just refuse to answer, the same as if you asked it to swear or say something racist.
Fair. Yes, people will eventually develop best practices through trial & error, and through better features/capabilities of the product/service itself. I just remain extremely cautious and pragmatic during this part of the hype cycle and zone of inflated expectations.
I think this is a trap a lot of people fall into: assuming that in ChatGPT solutions that have business value, ChatGPT output is delivered directly to a customer. While that may be true in some cases (e.g. companies that produce cheap blog marketing content should be running scared), it ignores the iceberg of use cases where a ChatGPT tool can simply be one in a set of tools, like Excel is.
Like think of a technical B2B product's technical support engineer who now has a personal assistant trained on the company's full catalog of technical documentation, who can provide instant answers to customer questions that the rep can validate before passing along to the customer on the phone.
Or an overworked public defender who now has an instant paralegal to proof documents or search for and summarize relevant case law that the human lawyer then reviews.
By the way, I asked ChatGPT itself about how it would respond to the message above. Its answer:
The statement you provided raises valid concerns about the capabilities and potential risks of advanced language models like ChatGPT Enterprise. Here's a response that addresses these concerns:
"Thank you for sharing your thoughts on ChatGPT Enterprise. It's important to recognize that advanced language models like ChatGPT do indeed have a remarkable capacity to generate human-like responses. However, they lack inherent understanding, critical thinking, and ethical judgment.
OpenAI acknowledges the concerns you've highlighted about accuracy, veracity, and potential consequences of generated content. It's crucial to understand that ChatGPT does not have an inherent "veracity bit" or a built-in mechanism to discern truth from falsehood. As such, it's important for users to exercise caution and discretion when relying on the output generated by the model.
In situations where accuracy and compliance are paramount, it's advisable to rely on established procedural logic, expert systems, and human review. ChatGPT is not a substitute for human oversight, particularly in sensitive areas such as legal, medical, or business transactions.
OpenAI is actively working to improve the control and guidance mechanisms for ChatGPT, and they acknowledge the importance of striking the right balance between automation and human review. As you pointed out, there are instances where generative AI is appropriate and others where traditional procedural logic and human intervention are necessary.
While ChatGPT can be a powerful tool for generating content and ideas, it is incumbent upon users to apply their expertise and judgment to evaluate the output, particularly in contexts where accuracy and adherence to policies are critical.
Your concerns align with ongoing discussions in the AI community about the ethical use and potential limitations of these technologies. As AI systems continue to advance, responsible development, transparency, and user education will remain important factors in their deployment."
It's important to remember that while AI models like ChatGPT have the potential to assist and enhance various tasks, they do not replace the need for human expertise, ethical considerations, and careful oversight in complex and critical scenarios.
You own and control your business data in ChatGPT Enterprise. We do not train on your business data or conversations, and our models don’t learn from your usage.
How can we be sure of this? Just take their word for it?
If you notice that some of your confidential info made it into next generations of the model, you'll be able to sue them for big $$$ for breach of contract. That's a pretty good incentive for them not to play stupid games with that.
Companies love to show how they care about privacy and security of their data yet the amount of data breaches are higher than ever before. What an irony
> The 80% statistic refers to the percentage of Fortune 500 companies with registered ChatGPT accounts, as determined by accounts associated with corporate email domains.
Yeah... I have no doubt that people at my Fortune 100 company tried it out with their corporate email domains. We have about 80,000 employees, so it seems nearly impossible that somebody wouldn't have tried it.
But, since then the policy has come down that nobody is allowed to use any sort of AI/LLM without written authorization from both Legal and someone at the C-suite level. The main concern is we might inadvertently use someone else's IP without authorization.
I have no idea how many other Fortune companies have implemented similar policies, but it does call the 80% number into question for me.
This is pretty standard for early-stage startups citing Fortune 500 use. Not representative and fairly misleading, but it's what they've done at most of the companies I've worked at.
The policy is specifically about third party AI/LLMs. I assume a locally running LLM would be okay as long as it was not trained by any material whatsoever external to the company. That is, we could only use our own IP to train it.
For those that need help migrating their models to OpenAI enterprise and ensure consistency results - would recommend checking the open source repo https://github.com/confident-ai/deepeval
This seems really cool, but I guess most companies in the EU won't dare to use this due to GDPR concerns and instead will opt-in for the Azure version, where you can choose to use GPT-models that are hosted in Azure's EU servers.
Is it really so hard for companies to provide a price range for Enterprise plan publicly on the pricing page?
Why can't I, as an individual, have the same features of an Enterprise plan?
What is the logic behind this practice other than profit maximization?
I'm willing to pay more to have unlimited high-speed GTP4 and Longer inputs with 32k token context.
EDIT: since I'm getting a lot of replies. Genuine question: how should I move to get a reasonable price as an individual for unlimited high-speed gpt4 and longer token context?
Because it’s often heavily negotiated. At the enterprise level, custom requests are entertained, and teams can spend weeks or months building bespoke features for a single client. So yeah, it’s kinda fundamentally impossible.
Oh yes. I'm willing to bet that it involves things like progressive discounts on # of tokens or # of seats, etc etc. This is just how you get access to the big bucks.
Because the truth is, each deal is custom packaged and priced for each enterprise. It's all negotiated pricing. Call it "value pricing" or whatever you want, prices are set at the tolerance level of each company. A price-sensitive enterprise might pay $50k while another company won't blink at $80k for essentially the same services.
I'm sure that's the correct answer, and that their very best was invested in analyzing the max profit strategy (as they should).
What I'm wondering if it means that the minimal price they can offer the service with at profit, is likely to be too steep for anyone like me, who interpret "talk to us" as the online equivalent of showing him the door. The other explanation I see is that there's not many in the camp of users who react to "talk to us" button by closing the tab instead of a deal, but I find that implausible.
> I'm wondering if it means that the minimal price they can offer the service with at profit, is likely to be too steep for anyone like me
I think the answer to that is "no". The problem is that they don't want to reveal the minimal price to their initial round of customers.
There are two basic ways you can think about pricing: cost-plus and value-minus. We programmers tend to like the former because it's clear, rational, and simple. But if you've got something of unknown value and want to maximize income, the latter can be much more appealing.
The "talk to sales" approach means they're going to enter into a process where they find the people who can get the most out of the service. They're going to try to figure out the total value added by the service. And they'll negotiate down from there. (Or possibly up; somebody once said the goal of Oracle was to take all your money for their server software, and then another $50k/year for support.)
Eventually, once they've figured out the value landscape, they'll probably come back for users like you, creating a commoditized product offering that's limited in ways that you don't care about but high-dollar customers can't live without. That will be closer to cost-plus. For example, note Github's pricing, which varies by more than 10x depending on what they think they can squeeze you for: https://github.com/pricing
> What is the logic behind this practice other than profit maximization?
Why would it be something else than profit maximization? It's a for-profit company, with stakeholders who want to maximize the possible profits coming from it, seems simple enough to grok, especially for users on S̵t̵a̵r̵t̵u̵p̵ ̵N̵e̵w̵s̵ Hacker News.
If it goes to the direction of Microsoft Copilot, then you can check out the recent announcement. Microsoft currently estimates that 30/user/month is a good list price to get „ChatGPT with all your business context“ to your employees.
There is the old silicon valley saying "This is a feature, not a product". Translated to the new AI age this is the moment were many startups will realize that what they were building wasn't a product but just a feature extension of chatGPT.
It's been discussed on twitter and /r/chatgpt but i've noticed it myself. I always find it funny when people say chatgpt hasn't changed since launch when i see it with my own eyes.
> The party told you to reject the evidence of your eyes and ears. It was their final, most essential command
I'm most curious about this: "weights were relaxed" Is that something you've seen with your own eyes? What does it even mean, how did you observe it? Seems hard to verify without proprietary information, if it even means something to begin with.
Or it means that the compute on the inference nodes is more efficient? Or that it’s tenanted in a way that decreases oversaturation? Or you’re getting programmatic improvements in the inference layer that are being funded by the enterprise spend?
How is this different from using GPT api on Azure? I thought that allowed you to keep you data corpus/documents private as well, ie not get sent to their servers for training
I used to be super hyped about ChatGPT and the productivity they could deliver. However the large amount of persistent bugs in their interface has convinced me otherwise.
Yes. If you re-open a chat from a while ago, typing a new message will typically result in an error causing me to having to start a new conversation. Happens both in the browser and on mobile for months now. Because no one else has it then it’s probably because I disabled history. It’s still a bug.
Another bug that I‘m having for weeks now is that pressing Stop Responding will indeed stop the stream but it will also cause a block on any new messages for about a minute. This one used to work just fine but started to fail a few weeks ago.
"From engineers troubleshooting bugs, to data analysts clustering free-form data, to finance analysts writing tricky spreadsheet formulas—the use cases for ChatGPT Enterprise are plenty. It’s become a true enabler of productivity, with the dependable security and data privacy controls we need."
I'm sorry but the financial analyst using chatGPT to write their excel formulas for them, and explicitly calling out that it is generating a formula that the analyst can't figure out on their own ("tricky") is an incredibly alarming thing to call out as a use case for chatGPT. I can't think of a lower reward, higher risk task to throw chatGPT at than financial analysis / reporting. Subtle differences in how things are reported in the financial world can really matter
Seems like they are quite startled with LLama 2 and Code Llama, and how its rapid adoption is accelerating the AI race to zero. Why have this when Llama 2 and Code Llama exists and brings the cost close to $0?
This sound like a huge waste of money for something that should just be completely on-device or self-hosted if you don't trust cloud-based AI models like ChatGPT Enterprise and want it all private and low cost.
But either way, Meta seems to be already at the finish line in this race and there is more to AI than the LLM hype.
> This sound like a huge waste of money for something that should just be completely on-device or self-hosted
I can imagine this argument being made repeatedly over the past several decades whenever anyone makes a decision to use any paid cloud service. There is a value in self-hosting FOSS services and managing it in house and there is a value in letting someone else manage it for you. Ultimately it depends on the business use case and how much effort / risk you are willing to handle.
If you could offer stable 70B llama API at half the price of ChatGPT API I would pay for it. I know HN likes to believe everything is close to $0, but it is hardly the case.
I get the self-host part, but if you had a dedicated machine would the ram be an issue? Can you run it on a machine with like 128GB of ram or the GPU equivalent?
agreed, and I can't wait for gpt4 to have great competition in terms of ease, price and performance. I was responding to this
> something that should just be completely on-device or self-hosted if you don't trust cloud-based AI models like ChatGPT Enterprise and want it all private and low cost
Less technical companies throw money at problems to solve them. Like mine, sadly... Even if it takes a small amount of effort, companies will throw money for zero effort.
Zero execution risk, rather than zero effort. There’s always a 10% chance that implementation goes on forever and spending some money eliminates that risk.
Maybe, but that's why things like ollama.ai are trying to fill the gap. It's simple, and you don't need all of the heavy weight enterprise crap if nothing ever leaves your system.
Interesting that they're still centered around Chat as the interface, with https://flowch.ai (our product) we're building it much more around projects and reports, which we think is often more suitable for businesses.
We're going after some of these use cases:
Want a daily email with all the latest news from your custom data source (or Google) for a topic?
How about parsing custom data and scores from your datasets using prompts with all the complicated bits handled for you, then downloading as a simple CSV?
Or even simply bulk generating content, such as generating Press Releases from your documents?
All easy with FlowChai :)
I think there's room for many different options in this space, whether that be Personal, Small Business or Enterprise.
Some feedback (it's clear you're just pitching FlowChai, but that's ok its HN):
I quick scrolled through your webpage and had no idea what it was. Extremely text heavy, and generic images that didn't communicate anything. I wanted to know what the product LOOKED like, especially as you're describing the difference between it and the chat interface of OpenAI.
I think you updated your comment (or I missed it) with the link to a "report" - it looked just like the output of one of the text bubbles except it had some (source) links (which I think Bing does as well)? It didn't seem all that different to me.
Very fair, we have demo videos, guides etc planned for the next week or so. As it's a tool that can do many things it's hard to describe. Still a lot to do :)
In terms of what makes the report different from Bing: this could be any source of data: scraped from the web, search, API upload, file upload etc, so there's a lot more power there. Also, it's not just one off reports, there's automation there which would allow for example a weekly report on the latest papers on GPT4 (or whatever you're interested in).
Doesn't seem to be in a usable state yet. I created an account and realised there's not actually any features to play with yet. I gave a URL for scheduled reports but I cannot configure anything about them.
You didn't offer me any way to delete my account and remove the email address I saved in your system. I hope you don't start sending me emails, after not giving me an ability to delete the account
From our discussions with enterprises (trying to sell our LLM apps platform), we quickly learned how sensitive enterprises are when it comes to sharing their data. In many of these organizations, employees are already pasting a lot of sensitive data into ChatGPT unless access to ChatGPT itself is restricted. We know a few companies that ended up deploying chatbot-ui with Azure's OpenAI offering since Azure claims to not use user's data (https://learn.microsoft.com/en-us/legal/cognitive-services/o...).
We ended up adding support for Azure's OpenAI offering to our platform as well as open-source our engine to support on-prem deployments (LLMStack - https://github.com/trypromptly/LLMStack) to deal with the privacy concerns these enterprises have.